In this study, we devise GLEAN within a novel encoder-bank-decoder architecture, which allows one to exploit the generative priors by needing just a single forward pass. An overview of the architecture is depicted in Fig. REF .
Given a severely downsampled LR image, GLEAN applies an encoder to extract latent vectors and multi-resolution convolutional features, which capture important high-level cues as well as spatial structure of the LR image.
Such cues are used to condition the latent bank, which further produces another set of multi-resolution features for the decoder.
Finally, the decoder generates the final output by integrating the features from both the encoder and the latent bank.
In this work, we adopt StyleGAN [1]}, [2]} as the generative latent bank due to its exceptional performance. The idea of latent bank can be extended to other generators such as BigGAN [3]}.
